In the last decade, medical segmentation methods have been typically based on deep artificial neural networks. In [1]} fully convolutional networks (FCNs) are constructed from locally connected layers including convolution, pooling, and upsampling. In their FCN architecture, the authors employed two main components: the downsampling and upsampling stages. The path to the downsampling stage captures contextual information while the upsampling path retrieves spatial information. U-Net [2]} originated from FCNs. The key difference is that each upsampling stage is connected via a concatenation operator to its corresponding downsampling stage. This way, each upsampling stage inherits the details on the corresponding downsampling stage, which leads to better segmentation performance. In SegNet, a novel technique is used for the upsampled encoder output, which involves storing the max-pooling indices at the pooling layer [3]}.
